Recent developments on the construction of spatio-temporal covariance models

Ma, Chunsheng

doi:10.1007/s00477-007-0154-x

Cited by 47 publications

(22 citation statements)

References 80 publications

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“…Thus, in our study, the attention has shifted to non-separable covariance structures, namely the product-sum and sum-metric models, which are widely used in the literature. Other parametric families of non-separable models are discussed in Cressie and Huang (1999), Ma (2008) and Rodrigues and Diggle (2010). For more general classes of non-separable covariance functions see Fonseca and Steel (2011) and Ip and Li (2015).…”

Section: Small-scale Variationmentioning

confidence: 99%

Modelling spatio-temporal data with multiple seasonalities: TheNO2Portuguese case

2017

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a b s t r a c tThis study aims at characterizing the spatial and temporal dynamics of spatio-temporal data sets, characterized by high resolution in the temporal dimension which are becoming the norm rather than the exception in many application areas, namely environmental modelling. In particular, air pollution data, such as NO 2 concentration levels, often incorporate also multiple recurring patterns in time imposed by social habits, anthropogenic activities and meteorological conditions. A two-stage modelling approach is proposed which combined with a block bootstrap procedure correctly assesses uncertainty in parameters estimates and produces reliable confidence regions for the space-time phenomenon under study. The methodology provides a model that is satisfactory in terms of goodness of fit, interpretability, parsimony, prediction and forecasting capability and computational costs. The proposed framework is potentially useful for scenario drawing in many areas, including assessment of environmental impact and environmental policies, and in a myriad applications to other research fields.

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Section: Small-scale Variationmentioning

confidence: 99%

Modelling spatio-temporal data with multiple seasonalities: TheNO2Portuguese case

2017

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“…There are two kinds of spatio-temporal covariance functions: separable and non-separable models [25,29]. In the separable model, the spatio-temporal covariance function is treated as either a sum or product of separate spatial and temporal covariance functions [30]. In the non-separable model, the spatio-temporal covariance function is treated as a non-linear, multiplicative version of the spatial and temporal covariance functions [6,10,[31][32][33].…”

Section: Related Workmentioning

confidence: 99%

“…The separable model is easy to implement; however, the space-time interaction may be not well considered. Although the non-separable model is able to consider the space-time interaction, in theory, the construction of the non-separable model for the non-stationarity space-time variable is very difficult [30]. In this study, spatial and temporal dimensions are both considered to calculate the interpolation results in spatial or temporal dimensions, e.g., the solution of Equations (10) and (11).…”

Section: Isprs Int J Geo-inf 2016 5 13 7 Of 14mentioning

confidence: 99%

A Hybrid Method for Interpolating Missing Data in Heterogeneous Spatio-Temporal Datasets

Deng

Zide

Liu

et al. 2016

IJGI

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Space-time interpolation is widely used to estimate missing or unobserved values in a dataset integrating both spatial and temporal records. Although space-time interpolation plays a key role in space-time modeling, existing methods were mainly developed for space-time processes that exhibit stationarity in space and time. It is still challenging to model heterogeneity of space-time data in the interpolation model. To overcome this limitation, in this study, a novel space-time interpolation method considering both spatial and temporal heterogeneity is developed for estimating missing data in space-time datasets. The interpolation operation is first implemented in spatial and temporal dimensions. Heterogeneous covariance functions are constructed to obtain the best linear unbiased estimates in spatial and temporal dimensions. Spatial and temporal correlations are then considered to combine the interpolation results in spatial and temporal dimensions to estimate the missing data. The proposed method is tested on annual average temperature and precipitation data in China (1984China ( -2009. Experimental results show that, for these datasets, the proposed method outperforms three state-of-the-art methods-e.g., spatio-temporal kriging, spatio-temporal inverse distance weighting, and point estimation model of biased hospitals-based area disease estimation methods.

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“…hich is statistically valid if both C S (h) and C T (u) are valid rnative is the metric model (Dimitrakopoulos a 3 A concerns whether or not the space and the time components of a formulated function are: separable such that they factor (Gneiting et al, 2006); or, nonseparable such that they form a linear combination (Ma 2008). Mitchell et al (2005) propose a modified multivariate repeated measures likelihood ratio test coupled with bootstrapping for this purpose.…”

Section: Space-time Autoregressive Structuresmentioning

confidence: 99%

“…S ce and aspects of spatial-temporal interaction, and proposes a new class of space-time covariances. Ma (2003Ma ( , 2008 presents methods for constructing spatio-temporal stationary covariance models, and supplements the set presented by Kolovos et al (2004). Gneiting et al (2006) posit theorems for symmetric and separable specifications, the Cressie-Huang and the Gneiting model, and stationarity.…”

Section: Space-time Autoregressive Structuresmentioning

confidence: 99%

Deriving Space-Time Variograms from Space-Time Autoregressive (STAR) Model Specifications

Griffith

Heuvelink

2012

Lecture Notes in Geoinformation and Cartography

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ABSTRACT:Many geospatial science subdisciplines analyze variables that vary over both space and time. The space-time autoregressive (STAR) model is one specification formulated to describe such data. This paper summarizes STAR specifications that parallel geostatistical model specifications commonly used to describe space-time variation, with the goal of establishing synergies between these two modeling approaches. Resulting expressions for space-time correlograms derived from 1 st -order STAR models are solved numerically, and then linked to appropriate space-time semivariogram models.

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Recent developments on the construction of spatio-temporal covariance models

Cited by 47 publications

References 80 publications

Modelling spatio-temporal data with multiple seasonalities: TheNO2Portuguese case

Modelling spatio-temporal data with multiple seasonalities: TheNO2Portuguese case

A Hybrid Method for Interpolating Missing Data in Heterogeneous Spatio-Temporal Datasets

Deriving Space-Time Variograms from Space-Time Autoregressive (STAR) Model Specifications

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